[Paper Review] Wireless Power Meets Energy Harvesting: A Joint Energy Allocation Approach

Xun Zhou, Chin Keong Ho+1 more|arXiv (Cornell University)|Oct 6, 2014

Energy Harvesting in Wireless Networks2 citations

TL;DR

This paper proposes a joint time and subcarrier allocation and power allocation strategy for OFDM-based wireless powered communication networks, where a user harvests energy from an access point to transmit data to a destination. By exploiting full or causal CSI, the approach maximizes the data rate at the destination through offline and online optimization algorithms, achieving significant rate gains over conventional schemes.

ABSTRACT

This paper investigates an orthogonal frequency division multiplexing (OFDM)-based wireless powered communication system, where one user harvests energy from an energy access point (EAP) to power its information transmission to a data access point (DAP). The channels from the EAP to the user, i.e., the wireless energy transfer (WET) link, and from the user to the DAP, i.e., the wireless information transfer (WIT) link, vary over both time slots and sub-channels (SCs) in general. To avoid interference at DAP, WET and WIT are scheduled over orthogonal SCs at any slot. Our objective is to maximize the achievable rate at the DAP by jointly optimizing the SC allocation over time and the power allocation over time and SCs for both WET and WIT links. Assuming availability of full channel state information (CSI), the structural results for the optimal SC/power allocation are obtained and an offline algorithm is proposed to solve the problem. Furthermore, we propose a low-complexity online algorithm when causal CSI is available.

Motivation & Objective

• To maximize the achievable data rate at the data access point (DAP) in a wireless powered communication system using OFDM.
• To address the challenge of time-varying and frequency-selective channels in both energy transfer (WET) and information transfer (WIT) links.
• To jointly optimize subcarrier allocation over time and power allocation across time slots and subcarriers for both WET and WIT links.
• To develop an offline algorithm under full channel state information (CSI) and a low-complexity online algorithm under causal CSI.
• To ensure interference avoidance at the DAP by scheduling WET and WIT over orthogonal subcarriers at each time slot.

Proposed method

• Formulates a joint optimization problem for subcarrier and power allocation across time slots and subcarriers to maximize the DAP's achievable rate.
• Uses convex optimization techniques to derive structural properties of the optimal solution under full CSI.
• Proposes an offline algorithm that solves the joint optimization problem by leveraging the derived optimality conditions.
• Designs a low-complexity online algorithm that adapts to causal CSI by using a greedy subcarrier assignment and power control policy.
• Enforces orthogonality between WET and WIT subcarriers at each time slot to prevent interference at the DAP.
• Applies water-filling-like power allocation principles to the WET and WIT links under subcarrier-specific channel gains.

Experimental results

Research questions

• RQ1 How can subcarrier and power allocation be jointly optimized to maximize the data rate in an OFDM-based wireless powered communication system?
• RQ2 What structural properties characterize the optimal subcarrier and power allocation under full CSI?
• RQ3 How can the optimization problem be solved efficiently when only causal CSI is available?
• RQ4 What performance gain is achievable by joint optimization compared to conventional orthogonal or fixed allocation schemes?
• RQ5 How does the proposed online algorithm maintain high data rates under time-varying channel conditions?

Key findings

• The optimal subcarrier allocation assigns subcarriers to WET or WIT based on channel gains, with a threshold-based rule derived from the Karush-Kuhn-Tucker conditions.
• The optimal power allocation follows a water-filling structure over subcarriers and time slots, prioritizing subcarriers with better channel conditions.
• The offline algorithm achieves the globally optimal rate by solving the joint optimization problem using convex relaxation and dual decomposition.
• The online algorithm achieves near-optimal performance with significantly reduced complexity, making it suitable for real-time implementation.
• Numerical results show that joint optimization outperforms conventional schemes in terms of achievable rate, especially in frequency-selective fading environments.
• The performance gain is most pronounced when the WET and WIT channels exhibit strong frequency selectivity and time variation.